Zero-shot Entity Linking by Reading Entity Descriptions
Lajanugen Logeswaran,Ming-Wei Chang,Kenton Lee,Kristina Toutanova,Jacob Devlin,Honglak Lee +5 more
- pp 3449-3460
TLDR
It is shown that strong reading comprehension models pre-trained on large unlabeled data can be used to generalize to unseen entities and proposed domain-adaptive pre-training (DAP) is proposed to address the domain shift problem associated with linking unseen entities in a new domain.Abstract:
We present the zero-shot entity linking task, where mentions must be linked to unseen entities without in-domain labeled data. The goal is to enable robust transfer to highly specialized domains, and so no metadata or alias tables are assumed. In this setting, entities are only identified by text descriptions, and models must rely strictly on language understanding to resolve the new entities. First, we show that strong reading comprehension models pre-trained on large unlabeled data can be used to generalize to unseen entities. Second, we propose a simple and effective adaptive pre-training strategy, which we term domain-adaptive pre-training (DAP), to address the domain shift problem associated with linking unseen entities in a new domain. We present experiments on a new dataset that we construct for this task and show that DAP improves over strong pre-training baselines, including BERT. The data and code are available at https://github.com/lajanugen/zeshel.read more
Citations
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Don't Stop Pretraining: Adapt Language Models to Domains and Tasks
Suchin Gururangan,Ana Marasović,Ana Marasović,Swabha Swayamdipta,Kyle Lo,Iz Beltagy,Doug Downey,Noah A. Smith,Noah A. Smith +8 more
TL;DR: It is consistently found that multi-phase adaptive pretraining offers large gains in task performance, and it is shown that adapting to a task corpus augmented using simple data selection strategies is an effective alternative, especially when resources for domain-adaptive pretraining might be unavailable.
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Scalable Zero-shot Entity Linking with Dense Entity Retrieval
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TL;DR: This article proposed GENRE, the first system that retrieves entities by generating their unique names, left to right, token-by-token in an autoregressive fashion, effectively cross encoding both context and entity name.
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KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation
TL;DR: The authors proposed a unified model for knowledge embedding and pre-trained LanguagE representation (KEPLER), which can not only better integrate factual knowledge into PLMs but also produce effective text-enhanced KE with the strong PLMs.
References
More filters
Proceedings Article
Attention is All you Need
Ashish Vaswani,Noam Shazeer,Niki Parmar,Jakob Uszkoreit,Llion Jones,Aidan N. Gomez,Lukasz Kaiser,Illia Polosukhin +7 more
TL;DR: This paper proposed a simple network architecture based solely on an attention mechanism, dispensing with recurrence and convolutions entirely and achieved state-of-the-art performance on English-to-French translation.
Proceedings ArticleDOI
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
TL;DR: BERT as mentioned in this paper pre-trains deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers, which can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks.
Proceedings ArticleDOI
Deep contextualized word representations
Matthew E. Peters,Mark Neumann,Mohit Iyyer,Matt Gardner,Christopher Clark,Kenton Lee,Luke Zettlemoyer +6 more
TL;DR: This paper introduced a new type of deep contextualized word representation that models both complex characteristics of word use (e.g., syntax and semantics), and how these uses vary across linguistic contexts (i.e., to model polysemy).
Book ChapterDOI
Domain-adversarial training of neural networks
Yaroslav Ganin,Evgeniya Ustinova,Hana Ajakan,Pascal Germain,Hugo Larochelle,François Laviolette,Mario Marchand,Victor Lempitsky +7 more
TL;DR: In this article, a new representation learning approach for domain adaptation is proposed, in which data at training and test time come from similar but different distributions, and features that cannot discriminate between the training (source) and test (target) domains are used to promote the emergence of features that are discriminative for the main learning task on the source domain.
Proceedings ArticleDOI
Universal Language Model Fine-tuning for Text Classification
Jeremy Howard,Sebastian Ruder +1 more
TL;DR: Universal Language Model Fine-tuning (ULMFiT) as mentioned in this paper is an effective transfer learning method that can be applied to any task in NLP, and introduces techniques that are key for finetuning a language model.